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CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

This is the official implementation code of the paper "CondLaneNet: a Top-to-down Lane Detection Framework Based on ConditionalConvolution". (Link: https://arxiv.org/abs/2105.05003) We achieve state-of-the-art performance on multiple lane detection benchmarks. Our paper has been accepted by ICCV2021.

Architecture,

Installation

This implementation is based on mmdetection(v2.0.0). Please refer to install.md for installation.

Datasets

We conducted experiments on CurveLanes, CULane and TuSimple. Please refer to dataset.md for installation.

Models

For your convenience, we provide the following trained models on Curvelanes, CULane, and TuSimple datasets

Model Speed F1 Link
curvelanes_small 154FPS 85.09 download
curvelanes_medium 109FPS 85.92 download
curvelanes_large 48FPS 86.10 download
culane_small 220FPS 78.14 download
culane_medium 152FPS 78.74 download
culane_large 58FPS 79.48 download
tusimple_small 220FPS 97.01 download
tusimple_medium 152FPS 96.98 download
tusimple_large 58FPS 97.24 download

Testing

CurveLanes 1 Edit the "data_root" in the config file to your Curvelanes dataset path. For example, for the small version, open "configs/curvelanes/curvelanes_small_test.py" and set "data_root" to "[your-data-path]/curvelanes".

2 run the test script

cd [project-root]
python tools/condlanenet/curvelanes/test_curvelanes.py configs/condlanenet/curvelanes/curvelanes_small_test.py [model-path] --evaluate

If "--evaluate" is added, the evaluation results will be printed. If you want to save the visualization results, you can add "--show" and add "--show_dst" to specify the save path.

CULane

1 Edit the "data_root" in the config file to your CULane dataset path. For example,for the small version, you should open "configs/culane/culane_small_test.py" and set the "data_root" to "[your-data-path]/culane".

2 run the test script

cd [project-root]
python tools/condlanenet/culane/test_culane.py configs/condlanenet/culane/culane_small_test.py [model-path]
  • you can add "--show" and add "--show_dst" to specify the save path.
  • you can add "--results_dst" to specify the result saving path.

3 We use the official evaluation tools of SCNN to evaluate the results.

TuSimple

1 Edit the "data_root" in the config file to your TuSimple dataset path. For example,for the small version, you should open "configs/tusimple/tusimple_small_test.py" and set the "data_root" to "[your-data-path]/tuSimple".

2 run the test script

cd [project-root]
python tools/condlanenet/tusimple/test_tusimple.py configs/condlanenet/tusimple/tusimple_small_test.py [model-path]
  • you can add "--show" and add "--show_dst" to specify the save path.
  • you can add "--results_dst" to specify the result saving path.

3 We use the official evaluation tools of TuSimple to evaluate the results.

Speed Test

cd [project-root]
python tools/condlanenet/speed_test.py configs/condlanenet/culane/culane_small_test.py [model-path]

Training

For example, train CULane using 4 gpus:

cd [project-root]
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29001 tools/dist_train.sh configs/condlanenet/culane/culane_small_train.py 4 --no-validate

Results

CurveLanes

Model F1 Speed GFLOPS
Small(ResNet-18) 85.09 154FPS 10.3
Medium(ResNet-34) 85.92 109FPS 19.7
Large(ResNet-101) 86.10 48FPS 44.9

CULane

Model F1 Speed GFLOPS
Small(ResNet-18) 78.14 220FPS 10.2
Medium(ResNet-34) 78.74 152FPS 19.6
Large(ResNet-101) 79.48 58FPS 44.8

TuSimple

Model F1 Speed GFLOPS
Small(ResNet-18) 97.01 220FPS 10.2
Medium(ResNet-34) 96.98 152FPS 19.6
Large(ResNet-101) 97.24 58FPS 44.8

Visualization results

Results